Multi‐modal models using fMRI, urine and serum biomarkers for classification and risk prognosis in diabetic kidney disease

Abstract Background Functional magnetic resonance imaging (fMRI) is a powerful tool for non‐invasive evaluation of micro‐changes in the kidneys. This study aims to develop classification and prognostic models based on multi‐modal data. Methods A total of 172 participants were included, and high‐resolution multi‐parameter fMRI technology was employed to obtain T2‐weighted imaging (T2WI), blood oxygen level dependent (BOLD), and diffusion tensor imaging (DTI) sequence images. Based on clinical indicators, fMRI markers, serum and urine biomarkers (CD300LF, CST4, MMRN2, SERPINA1, l ‐glutamic acid dimethyl ester and phosphatidylcholine), machine learning algorithms were applied to establish and validate classification diagnosis models (Models 1–6) and risk‐prognostic models (Models A–E). Additionally, accuracy, sensitivity, specificity, precision, area under the curve (AUC) and recall were used to evaluate the predictive performance of the models. Results A total of six classification models were established. Model 5 (fMRI + clinical indicators) exhibited superior performance, with an accuracy of 0.833 (95% confidence interval [CI]: 0.653–0.944). Notably, the multi‐modal model incorporating image, serum and urine multi‐omics and clinical indicators (Model 6) demonstrated higher predictive performance, achieving an accuracy of 0.923 (95% CI: 0.749–0.991). Furthermore, a total of five prognostic models at 2‐year and 3‐year follow‐up were established. The Model E exhibited superior performance, achieving AUC values of 0.975 at the 2‐year follow‐up and 0.932 at the 3‐year follow‐up. Furthermore, Model E can identify patients with a high‐risk prognosis. Conclusion In clinical practice, the multi‐modal models presented in this study demonstrate potential to enhance clinical decision‐making capabilities regarding patient classification and prognosis prediction.